docs: promote MOEA/D to the multi/many-objective default

The compare harness shows MOEA/D is the single most consistent
performer: top-3 on every multi- and many-objective table (convex,
disconnected, spherical and linear fronts; 2 through 10 objectives) and
fastest or near-fastest every time. No other algorithm is close to that
consistency. This matches the literature view of MOEA/D as a strong,
robust, scalable baseline -- with the known caveat that weight-vector
spread can leave gaps on highly irregular fronts (the DTLZ/ZDT suite
doesn't stress that).

But both decision trees buried it: the README filed it under "Want
decomposition / weight-vector style" -- a stylistic branch -- and framed
it as a speed pick; the book left it out of the TL;DR table entirely.
Meanwhile NSGA-II was the listed 2-3-objective default despite losing to
MOEA/D on every table and collapsing past ~4 objectives.

Both trees now lead the multi- and many-objective branches with MOEA/D,
keep NSGA-II as the well-understood alternative and the combinatorial
go-to, add MOEA/D to the TL;DR / quick-reference tables, and soften the
NSGA-III "strong default" framing to match the data.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

README.md

@@ -426,8 +426,20 @@ START
      │
      ├─ 2 or 3 (multi-objective)
      │    │
-     │    ├─ Strong default, fast, well-understood
+     │    ├─ Strong default — top-3 on every multi- and
-     │    │     → NSGA-II
+     │    │  many-objective table on the harness, fastest or
+     │    │  near-fastest every time
+     │    │     → MOEA/D   (decomposition into scalar sub-problems;
+     │    │                 robust across convex / disconnected /
+     │    │                 spherical / linear fronts and 2–10
+     │    │                 objectives. Caveat: weight-vector spread
+     │    │                 can leave gaps on highly irregular or
+     │    │                 degenerate fronts)
+     │    │     → NSGA-II  (canonical Pareto EA; well-understood and
+     │    │                 the established choice for combinatorial
+     │    │                 encodings — but edged out by MOEA/D on
+     │    │                 every MO table here, and fades past
+     │    │                 ~4 objectives)
      │    │
      │    ├─ Real-valued, smooth front, want best convergence
      │    │     → MOPSO    (multi-objective PSO; on the benches
@@ -435,7 +447,7 @@ START
      │    │                 convergence by 100× over the
      │    │                 dominance-based methods)
      │    │
-     │    ├─ Want better front quality than NSGA-II
+     │    ├─ Want better front quality than the default
      │    │     → IBEA     (indicator-based; consistently the best
      │    │                 of the dominance-based methods on these
      │    │                 benches — wins ZDT3 HV and DTLZ2 mean
@@ -448,9 +460,6 @@ START
      │    │                 fronts where exact HV-contribution is
      │    │                 the right discriminator)
      │    │
-     │    ├─ Want decomposition / weight-vector style
-     │    │     → MOEA/D   (very fast per generation, scales well)
-     │    │
      │    ├─ Disconnected front (separate arcs, e.g. ZDT3)
      │    │     → IBEA     (wins ZDT3 hypervolume on the harness;
      │    │                 MOEA/D and NSGA-II follow. Geometry-aware
@@ -469,19 +478,26 @@ START
      │          → PAES  (1+1 ES with a Pareto archive)
      │
      └─ 4+ (many-objective)
+          │
+          ├─ Strong default — #2 on every many-objective table on
+          │  the harness (DTLZ2 at 4 and 10 objectives, DTLZ1 at 8);
+          │  decomposition sidesteps the dominance collapse that
+          │  wrecks Pareto-based EAs at high objective count
+          │     → MOEA/D
+          │     (NSGA-II is the cautionary tale: on DTLZ2 at 10
+          │      objectives it finishes last — behind random search)
           │
           ├─ Linear / simplex-shaped front (e.g., DTLZ1)
           │     → GrEA       (grid coords drive ranking; on DTLZ1
           │                    here it beats NSGA-III by 3× and
-          │                    AGE-MOEA by 2.5×)
+          │                    AGE-MOEA by 2.5×, and wins the
-          │     → MOEA/D     (decomposition shines on linear fronts;
+          │                    8-objective DTLZ1 table outright)
-          │                    second on DTLZ1, also among the
+          │     → MOEA/D     (also #2 on both DTLZ1 tables)
-          │                    fastest per generation)
           │
           ├─ Curved / unknown front geometry
-          │     → NSGA-III   (reference-point niching, canonical;
+          │     → NSGA-III   (reference-point niching; canonical by
-          │                    a strong default when the front
+          │                    reputation, but MOEA/D outperforms it
-          │                    isn't simplex-shaped)
+          │                    on every harness table)
           │     → AGE-MOEA   (estimates L_p geometry per generation)
           │     → RVEA       (reference vectors with adaptive penalty)
           │
@@ -531,21 +547,21 @@ START
 
 | Algorithm     | Objectives | Strengths |
 |---|---|---|
-| **PAES**      | 2–3        | 1+1 ES with Pareto archive |
+| **MOEA/D**    | 2+         | decomposition; the most consistent all-rounder — top-3 on every MO/many-objective table here, fastest or near-fastest |
-| **NSGA-II**   | 2–3        | canonical Pareto-based EA |
+| **NSGA-II**   | 2–3        | canonical Pareto-based EA; well-understood, the go-to for combinatorial encodings — but fades past ~4 objectives |
-| **SPEA2**     | 2–3        | strength + density |
 | **MOPSO**     | 2–3        | multi-objective PSO; best convergence on smooth real-valued 2-obj fronts |
-| **IBEA**      | 2+         | indicator-based; consistently best of the dominance-based methods |
+| **IBEA**      | 2+         | indicator-based; consistently best of the dominance-based methods; wins disconnected fronts |
+| **SPEA2**     | 2–3        | strength + density |
 | **SMS-EMOA**  | 2+         | exact HV-contribution selection; high per-step cost, modest gain |
-| **HypE**      | 2+         | Monte Carlo HV estimation |
+| **HypE**      | 2+         | Monte Carlo HV estimation; strong on spherical many-objective fronts |
 | **ε-MOEA**    | 2+         | ε-grid archive; auto-sized |
 | **PESA-II**   | 2+         | grid-based region selection |
 | **AGE-MOEA**  | 2+         | adaptive front-geometry estimation |
 | **KnEA**      | 2+         | knee-point favored survival |
-| **MOEA/D**    | 2+         | decomposition; fast per-gen |
+| **PAES**      | 2–3        | 1+1 ES with Pareto archive |
 | **NSGA-III**  | 4+         | reference-point niching; strong on curved fronts |
 | **RVEA**      | 4+         | reference vectors with penalty |
-| **GrEA**      | 4+         | grid coords drive selection; particularly strong on linear/simplex fronts |
+| **GrEA**      | 4+         | grid coords drive selection; wins linear/simplex fronts at any objective count |
 
 ## Current algorithms
 

docs/book/src/choosing-an-algorithm.md

@@ -121,15 +121,27 @@ picker.
 
 ### Strong default
 
-[NSGA-II][Nsga2] is the canonical Pareto-based EA. Fast, well-understood,
+[MOEA/D][Moead] is the most consistent performer on the harness. It
-maintains diversity via crowding distance. On the harness it lands
+decomposes the problem into many scalar sub-problems (Tchebycheff or
-on the Pareto front of every test problem.
+weighted sum) and solves them in parallel — fast per generation, and
+robust: it finishes **top-3 on every multi- and many-objective table**
+(convex, disconnected, spherical and linear fronts; 2 through 10
+objectives) and is consistently the fastest or near-fastest. It rarely
+*wins* a table outright — a specialist usually does — but it never lands
+badly. One caveat from the literature: MOEA/D's spread depends on the
+weight-vector distribution and the scalarizing function, so it can leave
+gaps on highly irregular or degenerate fronts; the DTLZ/ZDT suite here
+doesn't stress that.
 
-NSGA-II is generic over the decision type — drop in
+[NSGA-II][Nsga2] is the other safe default — the canonical Pareto-based
-[`ShuffledPermutation`] + a permutation crossover and it solves
+EA: fast, well-understood, diversity-preserving via crowding distance.
-bi-objective TSP; drop in a binary initializer and [`BitFlipMutation`]
+On the harness it's edged out by MOEA/D on every multi-objective table
-and it solves bi-objective knapsack. See
+and degrades past ~4 objectives (see the many-objective section), but it
-[Multi-objective combinatorial problems](./cookbook/multi-objective-combinatorial.md).
+stays a solid 2–3-objective pick and is the established choice for
+*combinatorial* encodings: drop in [`ShuffledPermutation`] + a
+permutation crossover and it solves bi-objective TSP; drop in a binary
+initializer and [`BitFlipMutation`] and it solves bi-objective knapsack.
+See [Multi-objective combinatorial problems](./cookbook/multi-objective-combinatorial.md).
 
 ### Real-valued, smooth front, want best convergence
 
@@ -137,7 +149,7 @@ and it solves bi-objective knapsack. See
 hypervolume outright and converges 100× tighter than the
 dominance-based methods.
 
-### Better front quality than NSGA-II
+### Better front quality than the default
 
 [IBEA][Ibea] (indicator-based) is consistently the best of the
 dominance-based methods on the harness — wins ZDT3 hypervolume and
@@ -152,13 +164,6 @@ in theory; in practice on the harness budgets here it underperforms
 NSGA-II. Worth the higher per-step cost only when exact HV
 contribution is the right discriminator.
 
-### Decomposition / weight-vector style
-
-[MOEA/D][Moead] decomposes the multi-objective problem into many scalar
-sub-problems (Tchebycheff or weighted sum) and solves them in
-parallel. Very fast per generation; scales naturally to many
-objectives.
-
 ### Disconnected or non-convex front
 
 A *disconnected* front (separate arcs, like ZDT3) and a *non-convex but
@@ -194,18 +199,31 @@ population.
 
 ## Step 2 — many-objective (4+)
 
+### Strong default
+
+[MOEA/D][Moead] again. Decomposition sidesteps the *dominance resistance*
+that breaks Pareto-based methods at high objective count — each scalar
+sub-problem still has a clear best, even when almost every pair of
+solutions is mutually non-dominated. On the harness it is **#2 on every
+many-objective table** (DTLZ2 at 4 and 10 objectives, DTLZ1 at 8), and
+fast every time. [NSGA-II][Nsga2] is the cautionary tale: on DTLZ2 at 10
+objectives it finishes *last — behind random search* — because its
+crowding distance has no dominance signal left to refine.
+
 ### Linear / simplex-shaped front (e.g., DTLZ1)
 
-[GrEA][Grea] — grid coords drive ranking. On DTLZ1 it beats NSGA-III by
+[GrEA][Grea] — grid coords drive ranking. On 3-objective DTLZ1 it beats
-3× and AGE-MOEA by 2.5×.
+NSGA-III by 3× and AGE-MOEA by 2.5×, and it wins the 8-objective DTLZ1
+table outright.
 
-[MOEA/D][Moead] — decomposition shines on linear fronts; second on DTLZ1
+[MOEA/D][Moead] — also #2 on both DTLZ1 tables.
-and among the fastest per generation.
 
 ### Curved / unknown front geometry
 
-[NSGA-III][Nsga3] — reference-point niching; canonical many-objective method;
+[NSGA-III][Nsga3] — reference-point niching; the canonical many-objective
-strong default when the front isn't simplex-shaped.
+method by reputation, though on the harness MOEA/D outperforms it on
+every table. Reach for it when you specifically want reference-point
+niching.
 
 [AGE-MOEA][AgeMoea] — estimates L_p geometry per generation.
 
@@ -265,11 +283,12 @@ method on every algorithm in the catalog. See the
 | Multimodal single-objective continuous | [IPOP-CMA-ES][IpopCmaEs] or [Differential Evolution][DifferentialEvolution] |
 | Expensive single-objective | [Bayesian Optimization][BayesianOpt] or [TPE] |
 | Multi-fidelity single-objective | [Hyperband] |
-| 2- or 3-objective default | [NSGA-II][Nsga2] |
+| 2- or 3-objective default | [MOEA/D][Moead] (or [NSGA-II][Nsga2]) |
+| Many-objective default | [MOEA/D][Moead] |
 | 2-objective real-valued smooth front | [MOPSO][Mopso] |
-| Disconnected / non-convex front | [IBEA][Ibea] |
+| Disconnected front | [IBEA][Ibea] |
-| Many-objective default (curved front) | [NSGA-III][Nsga3] |
+| Many-objective, curved front | [NSGA-III][Nsga3] |
-| Many-objective linear / simplex front | [GrEA][Grea] |
+| Many-objective, linear / simplex front | [GrEA][Grea] |
 | Permutation problem (TSP with distance matrix) | [Ant Colony][AntColonyTsp] |
 | Generic permutation problem | [GA][GeneticAlgorithm] + permutation toolkit |
 | Bi-objective combinatorial (TSP / scheduling / knapsack) | [NSGA-II][Nsga2] + matching encoding operators |